The use of facial recognition for the indexing of images is now well established in the prior art. An example of such prior art is given in U.S. Pat. No. 6,526,158 to Goldberg, and further exemplified in U.S. Pat. No. 6,819,783 to Goldberg, et al.
It should be noted, however, that fully automated facial recognition is imperfect, and characterized by two types of errors. In the first case are false-positive errors, in which faces from two different people are assigned as being from the same person. In the second case are false-negative errors, in which faces from the same person are assigned as being from two separate people. One can usually trade off one type of error for another—that is, it is generally possible to reduce false-negative errors by increasing false-positive errors, and, conversely, to reduce false-positive errors by increasing false-negative errors. It is possible to eliminate false-positive errors altogether by never assigning two faces as being from the same person, and it is similarly possible to eliminate false-negative errors by assigning all faces as being from the same person. These extreme cases are not of practical importance though, and in general, all methods of automated facial recognition will generally exist with both false-positive and false-negative errors.
To achieve the goal of a fully indexed collection of images, manual assistance is required. This manual assistance can be from a person who knows the actual identities of the people represented in the image collection, such as in the indexing of a private image collection. Alternatively, as might occur in event photography (e.g. with cruise imaging), the final stages of indexing might be assisted instead by an employee of the cruise imaging company.
The difficulty in such manual processing can be appreciated when considering the numbers of images that can be present within a collection. For example, on a week-long cruise of a ship with more than 3000 passengers, upwards of 25,000 images may be taken, comprising 60,000 or more faces (an average of 2-3 people per picture). The number of possible face-to-face matches can be then over 3 billion. Automated facial recognition is imperfect, and depending on whether more false-positive or more false-negatives are acceptable, the number of sets of faces that must be reviewed in order to establish a perfectly or near perfectly indexed set may be as many as tens of thousands, taking hundreds of hours of labor. Even a personal collection of small thousands of images can take a substantial amount of time, reducing the attraction of facial recognition in indexing of images.
The methods and compositions of the present invention are intended to overcome these and other deficiencies, as described in the embodiments below.